



from collections.abc import Generator







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     0, 1
			while True:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     b, a + b
						yield b







def  lowerCAmelCase_	(lowerCAmelCase__:		int = 1_0_0_0  ):





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     1
			UpperCAmelCase_:		List[str]							      =     fibonacci_generator()
			while len(str(next(lowerCAmelCase__  )  )  ) < n:
						answer += 1
			return answer + 1


if __name__ == "__main__":
	print(solution(int(str(input()).strip())))











import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






